Heartbeat Signals Research Articles

An Improved Vital Signal Extraction Method Based on Laser Doppler Effect.

The mixed signal of respiratory waveform and heartbeat waveform detected by the Laser-Doppler system is processed with an intermediate-frequency (IF) interference filtering method, an enhanced extraction method and a waveform-fixing method. To filter the IF interference signals and the noise scatters in the time-frequency graph, the filtering method based on coefficient of variation (CoV) values and the enhanced curve extraction method based on noise-scatter theory are utilized in vital signal analysis. To decouple the respiratory signal and the heartbeat signal in time domain, the waveform-fixing method based on second-order difference theory is utilized in signal decoupling. This method as an algorithm is applied in the computer simulation and laboratory environments. The results show that the above methods can extract the mixed waveforms and identify the respiratory rates and heart rates in real experimental data. The IF interference signal can be filtered adaptively, and the accuracy of the analyzed rates can be improved to about 95%.

A high precision vital signs detection method based on millimeter wave radar

Millimeter wave (mmWave) radar technology has potential applications in vital signs detection and medicine. In order to minimize the influence of human micro-movements and respiratory harmonics on heart rate estimation, the vital signs detection method based on mmWave radar is studied in this paper. First, we use median filtering to eliminate baseline drift caused by human micromotion. Next, a differential recursive least squares multiple classification (DR-MUSIC) algorithm is proposed based on the combination of recursive least squares-based adaptive filter (RLS) and multiple signal classification (MUSIC) algorithm. This algorithm effectively suppresses respiratory harmonics and separates respiratory and heartbeat signals. Finally, heart rate value can be precisely estimated using spectral peak search. We invite a number of people to participate in the experiment, which demonstrate that the method successfully suppresse the impact of respiratory harmonics at low SNR. The error rate between the estimated heart rate and the reference heart rate is only 1.69% to 2.61%, which is significantly better than the existing algorithms.

Vital signs detection of moving targets using FMCW radar

Abstract Respiratory and heartbeat rates are crucial indicators for human health assessment. Compared to contact-based measurements, millimeter-wave radar detection of these vital signs avoids the discomfort caused by physical contact and better protects personal privacy, making it highly valuable for home health monitoring. The use of millimeter-wave radar for vital sign detection of the human body is mostly focused on targets in a stationary state at present. However, the human body may sway or even move during actual detection. This article proposes a non-contact vital sign detection method for moving targets. Compared with methods for detecting vital signs of stationary targets, detecting vital signs of moving targets requires determining the range bin where the targets are located continuously and extracting target phase information. The noise components such as movement and sway contained in the phase signal need to be removed. In this paper, moving target indication is used to remove static components, an adaptive range bin selection method is proposed to determine the range bin where the targets are located, and range bin selection fluctuation is smoothed using a moving average filter. The wavelet transform is used to decompose the phase signal, remove swaying noise based on autocorrelation function, and reconstruct the life signal for different scale factors. A bandpass filter is used to separate the respiratory and heartbeat signals, and a notch filter is designed to suppress respiratory harmonic signals. The experimental results show that the proposed method can separate vital signs signals from the phase signals of moving targets, achieving detection of respiration and heartbeat rate. The average accuracy of respiration and heartbeat rate detection is 94.7% and 95.5%, respectively.

3M: Measuring Vital Signs With Markov-Gauss Model.

Measuring vital signs (VS) contained in the echoes is crucial to the analyses of breathing and heartbeat signals using medical radar. Although many advanced signal processing algorithms have been developed for radar-based VS measurement and make some improved progress, existing schemes cannot achieve a good estimation of echo phases modulated by the respiratory and cardiac activities with high accuracy or low computation, and thus resulting in serious performance degradation on the subsequent separation of breathing and heartbeat patterns as well as the assessment of breathing rate (BR), heart rate (HR), and heart rate variability (HRV). In this paper, we propose a simple yet effective method to measure VS for medical radar, named 3M method. Specifically, our method firstly introduces the Markov-Gauss model to obtain the recursive expression of the echo phases carrying VS, and secondly derive a simple observation equation (SOE) to reflect the relationship between the observed signal and VS of radar measurement. Thirdly, the aforementioned Markov-Gauss model and SOE are fused by Kalman filter to measure VS with accurate estimation. The 3M method demonstrates an elegant structure, low complexity and excellent features introduced by Kalman filter. Simulation results show the superiority of 3M over other methods. Then, we conduct extensive experiments with insightful visualizations to validate the effectiveness of the 3M method. Comparative results on different scenarios illustrate that the 3M method not only achieves state-of-the-art VS measurement performance but also expresses robust properties to HRV analysis.

Ultrahigh Stretchable, Highly Transparent, Self-Adhesive, and Environment-Tolerant Chitin Nanocrystals Engineered Eutectogels toward Multisignal Sensors.

Addressing the conflict between achieving elevated mechanical stretchability and environmental adaptability is significant to a breakthrough in the practical application of flexible wearable materials. Therefore, inspired by the perceptive and protective properties of human skin, flexible wearable electronic skins (E-skins) based on deep eutectic solvent (DES) liquid and multiresponse eutectogel have been widely considered to be a promising platform for building a flexible wearable management system to achieve the purpose of "one stone, two birds". In this work, a multifunctional E-skin was designed based on an ultrastretchable, transparent, self-adhesive, and environmentally tolerant eutectogel by first incorporating cationized modified chitin nanocrystals into a covalently cross-linked polymer network comprised of the skeleton formed by a PAA polymerization network structure serving as a stretchable matrix and filled with DESs (ChCl:EG). The obtained eutectogel exhibits superhigh stretchability (up to 6707%), high toughness (17.7 MJ/m3), mechanical strength (0.48 MPa), self-adhesive, and high transparency (91.2%). Simultaneously, the multisignal sensor based on the above comprehensive properties and thermosensitive capacity exhibits a wide monitoring range, high strain/compression/temperature sensitivity, and good reproducibility. Remarkably, the sensor could be attached to rat hearts without glue or stickers for long-term monitoring of high-quality in vivo heartbeat signals. In this way, it is believed that the designed E-skin system based on eutectogel has great potential to serve as a promising platform for the next generation of flexible multisignal monitoring integrated wearable management systems.

Analysis of Heartbeat Signals to Detect Sleep Disorders Using Artificial Neural Network Methods

A human sleep disorder detection system has been designed using an AD8232 Electrocardiogram sensor module integrated with a microcontroller and internet connection through ESP 32. The heartbeat signals from the sensor are analyzed using Artificial Neural Network (ANN) methods to determine normal conditions, Obstructive Sleep Apnea (OSA), or Central Sleep Apnea (CSA). The sensor's accuracy was measured over 10 measurements, resulting in 96.85%. Testing with 30 training data samples achieved an accuracy of 93.33%, and testing with 20 training data samples achieved an accuracy of 80%. The system displays output values through the Internet of Things (IoT) with an average computation time of around 7.6 ms.

Enhanced Discrete Wavelet Transform–Non-Local Means for Multimode Fiber Optic Vibration Signal

Real-time monitoring of heartbeat signals using multimode fiber optic microvibration sensing technology is crucial for diagnosing cardiovascular diseases, but the heartbeat signals are very weak and susceptible to noise interference, leading to inaccurate diagnostic results. In this paper, a combined enhanced discrete wavelet transform (DWT) and non-local mean estimation (NLM) denoising method is proposed to remove noise from heartbeat signals, which adaptively determines the filtering parameters of the DWT-NLM composite method using objective noise reduction quality assessment metrics by denoising different ECG signals from multiple databases with the addition of additive Gaussian white noise (AGW) with different signal-to-noise ratios (SNRs). The noise reduction results are compared with those of NLM, enhanced DWT, and conventional DWT combined with NLM method. The results show that the output SNR of the proposed method is significantly higher than the other methods compared in the range of −5 to 25 dB input SNR. Further, the proposed method is employed for noise reduction of heartbeat signals measured by fiber optic microvibration sensing. It is worth mentioning that the proposed method does not need to obtain the exact noise level, but only the adaptive filtering parameters based on the autocorrelation nature of the denoised signal. This work greatly improves the signal quality of the multimode fiber microvibration sensing system and helps to improve the diagnostic accuracy.

A successive variational mode extraction method based on spectrum trend and its application in non‐contact vital sign detection

AbstractDetecting respiration and heartbeat information from human chest movement is crucial for non‐contact vital sign detection. Due to the small frequency difference between respiration and heartbeat signals and the relatively low amplitude of the heartbeat signal, it is challenging to separate and extract them completely. This letter proposes a successive variational mode extraction method based on spectrum trend for the extraction of respiration and heartbeat signals. The proposed method adaptively determines the initial centre frequency and extraction order of the desired signal modes by analyzing the spectrum trend of the signal to be decomposed. Additionally, a recursive framework is introduced to sequentially extract the desired signal modes. Both simulation and practical experiment results show that the method is effective in extracting respiration and heartbeat signals, significantly improving the accuracy of estimating respiration and heartbeat frequencies.

Precise measurement of human heart rate based on multi-channel radar data fusion

To achieve non-contact measurement of human heart rate and improve its accuracy, this paper proposes a method for measuring human heart rate based on multi-channel radar data fusion. The radar data were firstly extracted by human body position identification, phase extraction and unwinding, phase difference, band-pass filtering optimized by power spectrum entropy, and fast independent component analysis for each channel data. After overlaying and fusing the four-channel data, the heartbeat signal was separated using frost-optimized variational modal decomposition. Finally, a chirp Z-transform was introduced for heart rate estimation. After validation with 40 sets of data, the average root mean square error of the proposed method was 2.35 beats per minute, with an average error rate of 2.39%, a Pearson correlation coefficient of 0.97, a confidence interval of [-4.78, 4.78] beats per minute, and a consistency error of -0.04. The experimental results show that the proposed measurement method performs well in terms of accuracy, correlation, and consistency, enabling precise measurement of human heart rate.

Millimeter-Wave Radar-Based Identity Recognition Algorithm Built on Multimodal Fusion.

Millimeter-wave radar-based identification technology has a wide range of applications in persistent identity verification, covering areas such as security production, healthcare, and personalized smart consumption systems. It has received extensive attention from the academic community due to its advantages of being non-invasive, environmentally insensitive and privacy-preserving. Existing identification algorithms mainly rely on a single signal, such as breathing or heartbeat. The reliability and accuracy of these algorithms are limited due to the high similarity of breathing patterns and the low signal-to-noise ratio of heartbeat signals. To address the above issues, this paper proposes an algorithm for multimodal fusion for identity recognition. This algorithm extracts and fuses features derived from phase signals, respiratory signals, and heartbeat signals for identity recognition purposes. The spatial features of signals with different modes are first extracted by the residual network (ResNet), after which these features are fused with a spatial-channel attention fusion module. On this basis, the temporal features are further extracted with a time series-based self-attention mechanism. Finally, the feature vectors of the user's vital sign modality are obtained to perform identity recognition. This method makes full use of the correlation and complementarity between different modal signals to improve the accuracy and reliability of identification. Simulation experiments show that the algorithm identity recognition proposed in this paper achieves an accuracy of 94.26% on a 20-subject self-test dataset, which is much higher than that of the traditional algorithm, which is about 85%.

Intelligent deep model based on convolutional neural network's and multi-layer perceptron to classify cardiac abnormality in diabetic patients.

The ECG is a crucial tool in the medical field for recording the heartbeat signal over time, aiding in the identification of various cardiac diseases. Commonly, the interpretation of ECGs necessitates specialized knowledge. However, this paper explores the application of machine learning algorithms and deep learning algorithm to autonomously identify cardiac diseases in diabetic patients in the absence of expert intervention. Two models are introduced in this study: The MLP model effectively distinguishes between individuals with heart diseases and those without, achieving a high level of accuracy. Subsequently, the deep CNN model further refines the identification of specific cardiac conditions. The PTB-Diagnostic ECG dataset commonly used in the field of biomedical signal processing and machine learning, particularly for tasks related to electrocardiogram (ECG) analysis. a widely recognized dataset in the field, is employed for training, testing, and validation of both the MLP and CNN models. This dataset comprises a diverse range of ECG recordings, providing a comprehensive representation of cardiac conditions. The proposed models feature two hidden layers with weights and biases in the MLP, and a three-layer CNN, facilitating the mapping of ECG data to different disease classes. The experimental results demonstrate that the MLP and deep CNN based models attain accuracy levels of up to 90.0% and 98.35%, and sensitivity 97.8%, 95.77%, specificity 88.9%, 96.3% F1-Score 93.13%, 95.84% respectively. These outcomes underscore the efficacy of deep learning approaches in automating the diagnosis of cardiac diseases through ECG analysis, showcasing the potential for accurate and efficient healthcare solutions.

Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models

A botnet is a network of compromised computer systems, or bots, remotely controlled by an attacker through bot controllers. This covert network poses a threat through large-scale cyber attacks, including phishing, distributed denial of service (DDoS), data theft, and server crashes. Botnets often camouflage their activity by utilizing common internet protocols, such as HTTP and IRC, making their detection challenging. This paper addresses this threat by proposing a method to identify botnets based on distinctive communication patterns between command and control servers and bots. Recognizable traits in botnet behavior, such as coordinated attacks, heartbeat signals, and periodic command distribution, are analyzed. Probabilistic models, specifically Hidden Markov Models (HMMs) and Profile Hidden Markov Models (PHMMs), are employed to learn and identify these activity patterns in network traffic data. This work utilizes publicly available datasets containing a combination of botnet, normal, and background traffic to train and test these models. The comparative analysis reveals that both HMMs and PHMMs are effective in detecting botnets, with PHMMs exhibiting superior accuracy in botnet detection compared to HMMs.

Eccentric binaries: Periastron events and tidal heating

Context. Binary stars cannot be fully understood without assessing the interaction effects between the two components and the impact of these effects on observational diagnostics. Periastron brightening events, also known as the heartbeat phenomenon, are a clear manifestation of this type of interaction. Aims. We aim to explore the role of tidal shear energy dissipation in stars undergoing periastron brightening events. Methods. We performed a computation from first principles that uses a quasi-hydrodynamic Lagrangian scheme to simultaneously solve the orbital motion and the equations of motion of a 3D grid of volume elements covering the inner, rigidly rotating “core” of a tidally perturbed star. The equations of motion include the gravitational acceleration of both stars, the centrifugal, Coriolis, gas pressure accelerations, and viscous coupling between volume elements. The method is illustrated for a grid of model binary systems with a 10 M⊙ primary that is perturbed by a 6.97 M⊙ companion in eccentric orbits (e = 0 − 0.7). The model is then applied to the heartbeat star MACHO 80.7443.1718. Results. We find an increase by factors ∼10−6–10−3 in tidal shear energy dissipation at periastron, consistent with the majority of observed heartbeat stars. The magnitude of the periastron effect correlates with the degree of departure from synchronicity: stars rotating much faster or much slower than the synchronous rate at periastron present the strongest effect. We confirm that for eccentricities ≤0.3, pseudo-synchronization occurs for 0.8 < ω/Ωave < 1, where Ωave is the average orbital angular velocity. The minimum energy rotation rate (pseudo-synchronism) for e = 0.5 and 0.7 occurs for 1.0 < ω/Ωave < 1.15. The tidal shear energy dissipation model reproduces from first principles the ∼23% maximum brightness enhancement at periastron of MACHO 80.7443.1718. Conclusions. Our results suggest that the magnitude and shape of the heartbeat signal may serve as diagnostics for the internal stellar rotation and turbulent viscosity values.

Comparative Performance Analysis of Time-Frequency Domain Images and Raw Signal Data for Classification of ECG Signals

ECG signals are one of the most common tools used to diagnose cardiovascular diseases. ECG signals are obtained by measuring electrical changes on the skin surface. Arrhythmias occurring in the heart are diagnosed because the expert evaluates ECG signals. This diagnosis depends on the experience of the specialist and is a subjective evaluation. With the widespread use of computer-aided diagnostic systems, evaluations dependent on the expert's experience are objectified, and support is provided to the physician for diagnosis. For computer-aided ECG classification, beats are detected from ECG signals, and arrhythmias are detected by analyzing the structure of these beats. In recent years, deep learning models have been successful in classifying ECG signals. The data to be used in the classification process is realized with the help of morphological features or images of the signal. The main objective of this study is to compare the classification performance of digital and visual heartbeat data for ECG signal classification. For this purpose, 1D-CNN and 2D-CNN architectures are used for the type of ECG signals. As inputs of the 1D-CNN model, numerical values of the heartbeat signal and hand-crafted features obtained from these numerical values were used. The inputs of the 2D-CNN model are the raw signal image, spectrogram, scalogram, Mel-spectrogram, GFCC, and CQT images, which are visual representations of the heartbeat signal. The results show that the numerical model of the ECG signal fails for classification, while the hand-crafted features provide 85.2% accuracy. The results obtained with the visual representation of the signal provided over 99% classification accuracy for all images. The highest success rate was 99.9% with the visualization of the raw signal. In line with these findings, the 2D-CNN architecture and the visual representation of the heartbeat signal were found to be the most suitable method for classifying ECG signals.

IoT-Based Virtual Primary Clinic: Remote Patient Monitoring and Consultation System

Using Internet of Things technology, the Virtual Primary Clinic project is a cutting-edge healthcare endeavor that enables remote patient monitoring and consultation. The project collects essential health metrics including body temperature, oxygen saturation, heartbeat, and ECG signals by integrating a variety of sensors, including NodeMCU ESP32, SpO2, DHT11, and AD8232 ECG. The information collected by these sensors is sent via API to a specially created website and an online platform called ThingSpeak. With features including patient registration, appointment scheduling, access to medical histories, and doctor-patient contact, the website acts as a comprehensive interface between patients and physicians. Patients can register on the website, provide their medical information, and get medications sent to them remotely based on their conditions. However, without having to see patients in person, doctors can examine patient data, diagnose ailments, and write prescriptions or offer medical advice.The Virtual Primary Clinic project intends to improve healthcare accessible, especially in remote or underserved locations, by enabling remote monitoring and consultation. In addition to providing healthcare providers with an effective platform for remote patient care, it gives patients the ability to take charge of their health. This project is a major step toward using cutting-edge IoT-based technologies to improve patient outcomes and democratize healthcare.

A PRECISE RESPIRATORY AND HEART RATE DETECTION METHOD FOR MILLIMETER-WAVE RADAR

Millimeter wave radar as a type of noncontact sensor can more conveniently and insensibly obtain breathing and heartbeat signals. However, due to the weak radar echo signal and the influence of noise, there may be phase ambiguity and separation distortion of breathing and heartbeat signals during the processing, which affects the accuracy of detection. In this paper, an enhanced extended differential and cross multiplication (EDACM) algorithm is presented to obtain chest position signals without phase ambiguity. A specific order finite impulse response (FIR) filter with a Blackman window is applied to extract breath signals without waveform distortion from phase signals, and the heartbeat signal without rate distortion is extracted from the phase signal by means of the designed Chebyshev II filter. The measured results show that the proposed method achieves 96.78% accuracy for heart rate detection and 94.44% accuracy for respiratory rate detection, and improves the accuracy of heart rate detection by 2.22% and respiratory rate detection by 4.06% compared to the band-pass filter method under the same parameter conditions.

Chest Wall Motion Model of Cardiac Activity for Radar-Based Vital-Sign-Detection System.

An increasing number of studies on non-contact vital sign detection using radar are now beginning to turn to data-driven neural network approaches rather than traditional signal-processing methods. However, there are few radar datasets available for deep learning due to the difficulty of acquiring and labeling the data, which require specialized equipment and physician collaboration. This paper presents a new model of heartbeat-induced chest wall motion (CWM) with the goal of generating a large amount of simulation data to support deep learning methods. An in-depth analysis of published CWM data collected by the VICON Infrared (IR) motion capture system and continuous wave (CW) radar system during respiratory hold was used to summarize the motion characteristics of each stage within a cardiac cycle. In combination with the physiological properties of the heartbeat, appropriate mathematical functions were selected to describe these movement properties. The model produced simulation data that closely matched the measured data as evaluated by dynamic time warping (DTW) and the root-mean-squared error (RMSE). By adjusting the model parameters, the heartbeat signals of different individuals were simulated. This will accelerate the application of data-driven deep learning methods in radar-based non-contact vital sign detection research and further advance the field.

Contactless and short‐range vital signs detection with doppler radar millimetre‐wave (76–81 GHz) sensing firmware

AbstractVital signs such as heart rate (HR) and respiration rate (RR) are essential physiological parameters that are routinely used to monitor human health and bodily functions. They can be continuously monitored through contact or contactless measurements performed in the home or a hospital. In this study, a contactless Doppler radar W‐band sensing system was used for short‐range, contactless vital sign estimation. Frequency‐modulated continuous wave (FMCW) measurements were performed to reduce the influence of a patient's micromotion. Sensing software was developed that can process the received chirps to filter and extract heartbeat and breathing rhythm signals. The proposed contactless sensing system eliminates the need for the contact electrodes, electric patches, photoelectric sensors, and conductive wires used in typical physiological sensing methods. The system operates at 76–81 GHz in FMCW mode and can detect objects on the basis of changes in frequency and phase. The obtained signals are used to precisely monitor a patient's HR and RR with minimal noise interference. In a laboratory setting, the heartbeats and breathing rhythm signals of healthy young participants were measured, and their HR and RR were estimated through frequency‐ and time‐domain analyses. The experimental results confirmed the feasibility of the proposed W‐band mm‐wave radar for contactless and short‐range continuous detection of human vital signs.

Non-Contact Measurement of Cardiopulmonary Activity Using Software Defined Radios.

Vital signs are important indicators to evaluate the health status of patients. Channel state information (CSI) can sense the displacement of the chest wall caused by cardiorespiratory activity in a non-contact manner. Due to the influence of clutter, DC components, and respiratory harmonics, it is difficult to detect reliable heartbeat signals. To address this problem, this paper proposes a robust and novel method for simultaneously extracting breath and heartbeat signals using software defined radios (SDR). Specifically, we model and analyze the signal and propose singular value decomposition (SVD)-based clutter suppression method to enhance the vital sign signals. The DC is estimated and compensated by the circle fitting method. Then, the heartbeat signal and respiratory signal are obtained by the modified variational modal decomposition (VMD). The experimental results demonstrate that the proposed method can accurately separate the respiratory signal and the heartbeat signal from the filtered signal. The Bland-Altman analysis shows that the proposed system is in good agreement with the medical sensors. In addition, the proposed system can accurately measure the heart rate variability (HRV) within 0.5m. In summary, our system can be used as a preferred contactless alternative to traditional contact medical sensors, which can provide advanced patient-centered healthcare solutions.

Non-Contact Heartbeat and Respiration Signal Detection Based on Improved Variational Mode Extraction

Non-Contact Heartbeat and Respiration Signal Detection Based on Improved Variational Mode Extraction